With the progress of the times and the continuous development of various technologies, great changes have taken place in our daily life. The wide application of big data technology and artificial intelligence technology has made our life more convenient and fast. Based on this, the intelligent traffic management mode can be created, which can make our current traffic congestion more environmental protection convenient. The problem of traffic jam can be solved effectively, so that the traffic field of our country can achieve standardized development and improve the efficiency of traffic management.
Wireless sensor technology and artificial intelligence recognition technology are the main ways of object perception and identification, and are also the basic technical conditions for building intelligent transportation. Intelligent recognition is a kind of identification tag which can represent the identity of the object, such as two-dimensional code or bar code. It records the unique location information and characteristics in the relevant electronic tags. Then it can identify these information accurately through artificial intelligence equipment, and then upload the read information to the control system center for analysis and decision-making. Wireless sensor network is mainly composed of a large number of micro-sensors in the monitoring target area, which constitute a comprehensive monitoring network. The main advantages of wireless sensor network are easy deployment, low-cost operation and flexible layout. Sensors in intelligent traffic mainly include two parts: convergence node and acquisition node. For example, each individual acquisition point is actually a small information processing system, which can automatically collect data information in the responsible area, and then transmit all kinds of information environmental protection collected to other nodes, or to the node sink center, which sends comprehensive information to the processing center for unified processing.
At present, each module in intelligent transportation system is still in a state of separate operation and information separation, which can not promote the effective connection between various data and information, leading to a serious phenomenon of data waste. Intelligent traffic cloud is a management technology that integrates cloud computing with traffic service as its main goal. It also has the advantages of unified analysis of resources, information security and mass information storage in cloud computing. It provides an effective channel for urban traffic data management and sharing. In fact, cloud computing means that a large number of high-speed computers are centralized in the network, thus forming a large virtual resource management site, which can provide storage and analysis computing services for remote network end users. Users can rent cloud computing services provided by service providers without purchasing various independent hardware. Similar to cloud services, cloud services in ITS can be divided into software services, platform services and infrastructure services. Cloud processing platform is also the main research direction of intelligent transportation. It can analyze, calculate and store massive data, so as to reduce the pressure of real-time data storage and improve the development potential.
Data information in intelligent traffic has the characteristics of heterogeneity, diversity and mass, which increases the difficulty of data information processing. Data collection of vehicles and various traffic facilities, complex work such as detection and judgment in traffic incidents, can not be separated from data processing. The common processing technologies in intelligent transportation include data visualization, data activation, data mining, data fusion and so on. In addition, data should be uploaded selectively so as to maintain personal privacy. Data fusion also involves data processing technology in decision-making, communication and artificial intelligence. It can detect multi-source environmental protection information from three perspectives: decision-making layer, feature layer and data layer. Because the process of data fusion involves a large number of sensors and information acquisition, the data space and data time should be pre-processed before the formal fusion work. By aligning space-time, the confusion of data management can be effectively avoided, and the reliability and consistency of data can be effectively promoted. Improvement.